GPT Researcher vs search-boost
GPT Researcher: GPT Researcher is an open-source LLM agent designed for deep, structured research on any topic using web and local sources. It produces detailed reports with citations by running parallel research agents, scraping relevant sources, filtering content, and synthesizing findings. Supports local document research, customizable report formats, and multiple LLM backends including OpenAI, Anthropic, and Ollama.; search-boost: SearchBoost is a multi-engine web search and evidence synthesis tool specifically designed for AI coding agents. It provides a unified core runtime across various host ecosystems, enabling parallel multi-agent research workflows with zero-config onboarding and strong security.
TL;DR
- You want an Apache-2.0 agent that plans sub-questions, searches the web and local documents in parallel, and writes 2,000+ word reports with citations.
- You want to use it as a Python library, standalone web app, or MCP-connected research skill with PDF/Word export.
- You want a general-purpose super-agent harness with a bash sandbox, persistent memory, and Feishu/WeCom/Slack bots; DeerFlow is built for that.
- You do not want per-report search API (e.g. Tavily) and multi-call LLM token costs.
Empowering AI coding agents with advanced web search.
Side-by-Side Comparison
GPT Researchersearch-boostFeatures
GPT Researchersearch-boostUse Cases
GPT Researchersearch-boostBest For
GPT Researchersearch-boostFAQ
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